Building a Cost-Effective Data Platform for Small Businesses
Written on
Chapter 1: Introduction to Data Platforms
This article targets small enterprises lacking a dedicated data team or those with limited resources for managing a data platform. The emphasis here is on creating a data platform tailored for small businesses without a significant financial outlay. All that’s required is a laptop and some basic technical skills.
Before diving into the setup process, it's crucial to identify the most suitable data platform for your organization. Given the vast amount of information available about data platforms, it's important to recognize that there is no one-size-fits-all solution. Building a platform involves using specific components that cater to your company's needs.
Data platforms can vary significantly from one organization to another. When developing the ideal data platform, you must consider factors such as your company's culture, business goals, and organizational structure.
Building a data platform begins with asking key questions about your organization: Do you require a central repository for all your data that facilitates acquisition, storage, delivery, and governance while ensuring security throughout the data lifecycle? Let's explore some critical inquiries.
How will you gain stakeholder buy-in?
A data platform is only beneficial if its users—stakeholders across the organization—are familiar with and supportive of it. Engaging all potential users before launching the platform is essential for ensuring its effectiveness. Employees from various departments should recognize how the platform can add value to their work. The data team’s initial responsibility is to communicate this value and establish metrics for success as the company scales.
Who owns what within the data stack?
Understanding data ownership is vital. How will the data be utilized? Will it be a shared asset accessible throughout the organization? Various teams may control different stages of the data lifecycle; for instance, the data team might manage raw data before handing it off to the marketing team for analysis, which can then be visualized on a dashboard for executives.
The comprehensive data stack comprises multiple components supporting each team.
How will you assess success?
Measuring the effectiveness of the data platform is crucial. It's important to determine how stakeholders can utilize the data to meet business needs and evaluate the data team's performance in terms of quality and efficiency.
Will you centralize or decentralize your data platform?
Should your organization consolidate its data team? Will centralization create excessive bottlenecks, or will a decentralized model lead to duplication and complexity? Understanding the implications of each structure is vital in deciding the best approach for your data platform.
How will you ensure data reliability and trust?
As data volumes grow, ensuring reliability becomes increasingly important. Whether you choose to develop your own reliability tools or purchase existing ones, this component will be crucial for a functional data platform.
Chapter 2: Technology Considerations
Let’s explore the technological aspects you need to address before constructing a data platform. Here are some thoughts on the topic:
Incremental Development
Start by designing your data platform incrementally. If an issue arises at a particular step, you can revert to the previous stage without needing to redo the entire process. When managing large datasets, it becomes evident that an incremental approach is essential.
Lego Block Approach
Instead of writing new code for every problem, consider utilizing existing components to address issues. Design a data platform that minimizes the need for extensive coding. The less custom code you create, the better, especially if you’re working solo. Excessive custom coding leads to increased maintenance and complexity.
Effective Monitoring
Once your initial data pipeline is operational, it’s crucial to establish proper alerting and monitoring systems. You want to be alerted about issues before they escalate and affect users. Implement high-level alerts and treat them as production incidents to prioritize error management.
Data Product Management
Managing Data Product Management as a solo data team member can be challenging. This role requires a blend of client empathy and technical expertise. Familiarity with database structures and SQL queries will be beneficial in executing this role effectively.
Chapter 3: Establishing Your Data Platform
My philosophy aligns with the #simpleit approach, which advocates for minimizing IT systems while maximizing service provision. This principle is equally applicable to data platforms.
The Basic Pipeline
The foundational pipeline involves writing a script to extract data, typically done using Python. Data is loaded into a MySQL database on your laptop, requiring manual execution for new data extractions. For reporting, tools like Google Data Studio can be connected to your local setup.
The Basic Data Pipeline in the Cloud
You can adapt the same script to run as a Cloud Function on Google Cloud Platform. By scheduling the function to run a few times daily, you can stay within the free tier. Instead of MySQL, you would use BigQuery, with Cloud Storage serving as a data lake for file storage.
The No-Budget Open Source Data Platform
For those managing a one-person data team, an effective data platform can be constructed using open-source tools like Airbyte and Superset, both of which can be run using Docker.
Airbyte
Airbyte is an open-source data integration tool that simplifies the setup of ELT data pipelines with minimal coding. It allows you to connect various data sources and destinations effortlessly, with many pre-built connectors available.
Superset
Apache Superset is an open-source tool for data exploration and visualization, enabling users to create dashboards and automate reporting to stakeholders.
Setting Up the Data Platform with Docker
Setting up this data platform locally is straightforward. Begin by downloading Docker Desktop and cloning both Airbyte and Apache Superset from their respective GitHub repositories.
Airbyte Quick Start
To quickly start with Airbyte, execute the following commands:
$ cd airbyte
$ docker-compose up
Superset Quick Start
For Superset, follow the documentation, and use the commands below:
$ cd superset
$ docker-compose -f docker-compose-non-dev.yml up
This setup launches Airbyte and Superset containers in Docker Desktop, allowing you to leverage your local MySQL instance for data loading and dashboard creation. You can also utilize Airbyte’s BigQuery connector for additional reporting options.
Conclusion
This approach offers a flexible method for establishing your data pipeline. As your needs grow, you can incorporate additional components like dbt and Airflow.
For small teams, it’s wise to start with a few essential tools and expand as demand increases. Avoid getting sidetracked by new technologies or external influences; focus on your core objective of delivering a functional data platform for your organization. Engaging with overly opinionated colleagues may lead you off track.
As a solo data team member, your time is precious—stick to the tools that enable you to get straight to the critical aspects of your work without unnecessary complications.